1. Field of the Invention
This invention is concerned with a method for generating subsurface models of lithology using a Monte Carlo procedure. The lithologic models are obtained by combining seismic attribute records collected along an array of seismic stations that are deployed over an area of interest and from lithology observations in boreholes located within or close to the area covered by the seismic survey.
2. Discussion of Related Art
Although the art of seismic exploration is well known, those principles that are germane to this invention will be reviewed briefly. An acoustic wavefield is generated at or near the surface of the earth to insonify the underlying earth layers or strata. The wavefield is reflected in turn from each subsurface stratum whence the wavefield returns to the surface. The returning reflected wavefield manifests itself as a periodic mechanical motion of the earth's surface that is detected by suitable sensors. The sensors convert the mechanical motion to electrical signals which are recorded on an archival storage medium such as time-scale recordings in analog or digital format as desired by the investigator.
Of the many quantities that may be gleaned from the recorded seismic data, two are of particular interest, namely: Reflection travel time and reflection amplitude. Reflection travel time is a measure of the depth of the respective strata. Reflection amplitude depends upon the reflectivity coefficient at the interface between two strata. The reflectivity coefficient depends upon the difference in acoustic impedance across the interface. The acoustic impedance of a stratum is defined by the product of the acoustic velocity and the density of that rock layer. Impedance is measured in units of meters per second per gram per cubic centimeter.
To be of use quantitatively, the observed reflection amplitude must be corrected for spherical spreading, instrumental artifacts and other predictable effects to provide true amplitude measurements. The resulting true amplitudes may used to calculate the acoustic impedances of the respective strata.
It can be demonstrated that limited ranges of acoustic impedance values can be associated with particular rock types such as, by way of example but not by way of limitation, sand, shale and dolomite. However, because of seismic and lithologic noise, there is overlap between the ranges of impedance values attributable to each lithoclass. Thus, any one calculation of acoustic impedance is at best only an estimate of a particular lithoclass, the estimate being subject to statistical uncertainty.
In the course of geoexploration, control points may be established by boreholes, often quite widely separated, that penetrate strata of interest. At such sparse control points, the seismic observations may be calibrated by comparison of selected seismic attributes with a study of the texture and composition of the target strata. The desideratum of a seismic survey line, having relatively closely-spaced observation stations that are distributed between the sparse control points, is to estimate the continuity of one or more target lithologic horizons between the control points.
U.S. Pat. No. 4,926,394 issued May 15, 1990 to Phillipe M. Doyen and assigned to the assignee of this invention, teaches a type of Monte Carlo statistical method for estimating a variation in rock type or texture, that is, the change in lithology along a given stratum or a related grouping of strata within a selected geologic formation. The estimates are based on seismic data gathered over an array of survey lines that coincide with sparsely-spaced control points such as boreholes. This is a type of maximum a posteriori estimation technique. It suffers from the disadvantages that a) it is computer intensive; b) it is sometimes difficult to guarantee convergence of the iterative optimization procedure used in the technique; c) it is difficult to specify the required lithology transition statistics.
A number of methods have been suggested that are based on image enhancement techniques such as discussed in the paper The Mathematical Generation of Reservoir Geology by C. L. Farmer, presented July, 1989 at the IMA/SPE European Conference on the Mathematics of Oil Recovery.
Another technique involves Indicator Kriging such as is discussed in a paper entitled Mapping by Simple Indicator Kriging by Andrew R. Solow in Mathematical Geology, v. 18, no. 3, 1985. That method is a generalized linear regression technique to calculate a lithoclass probability distribution function at one location from lithoclass data at other locations in the pertinent space of interest. The method requires the specification of a spatial correlation model which may be difficult to infer from sparse borehole control. Simple Indicator Kriging does not allow direct integration of seismic information. Also the models are poorly defined if well control is sparse. The term "indicator" implies that the lithoclasses are discrete variables such as sand or shale rather than continuous variables such as permeability.
There is a need for a method that will generate simulated models of subsurface lithology by combining seismic attribute data and lithologic observations in boreholes. The method should be easy to generalize in the presence of multivariate environments.